Today, a lot of work is executed on data in a simple fashion to generate value out of it. However, huge amount of data is semi-structured or un-structured making it very difficult for a user to work. Semi-structured data is a form of structured data that does not conform to the formal structure of data models associated with relational database or other forms of data tables. Such data needs to be cleaned up before making it available to users.
To overcome the problem of semi-structured data, a manual cleanup (parsing) process exists that cleans up (also known as wrangling) semi-structured data through a tool (user interface driven tools). However, the manual cleanup of the semi-structured data takes time and ultimately delays the insight that data might provide to a user like business analyst. Additionally, for data that is extensive in nature, the manual cleanup can be a very labor intensive task and consumes lots of manual time. Other than manual and time consuming drawbacks, the parsing is also prone to user error as it is a long and laborious process.
In view of the above, there is a need for an efficient method for generating structured data from semi-structured data.